[1]徐先峰,郑少杰,赵 依,等.基于数据分解与重构的光伏发电功率超短期预测[J].机械与电子,2022,(04):20-25.
　XU Xianfeng,ZHENG Shaojie,ZHAO Yi,et al.Ultra-short-term Prediction of Photovoltaic Power Generation Based on Data Decomposition and Deconstruction[J].Machinery & Electronics,2022,(04):20-25.
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基于数据分解与重构的光伏发电功率超短期预测()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年04期
页码:
20-25
栏目:
设计与研究
出版日期:
2022-04-27

文章信息/Info

Title:
Ultra-short-term Prediction of Photovoltaic Power Generation Based on Data Decomposition and Deconstruction
文章编号:
1001-2257 ( 2022 ) 04-0020-06
作者:
徐先峰; 郑少杰; 赵 依; 王世鑫; 蔡路路
长安大学电子与控制工程学院,陕西 西安 710064
Author(s):
XU Xianfeng ;  ZHENG Shaojie ;  ZHAO Yi ;  WANG Shixin ;  CAI Lulu
( School of Electronics and Control Engineering , Chang ’ an University , Xi ’ an 710064 , China )
关键词:
信号处理; 深度学习; 深度信念网络; 序列到序列算法; 组合模型
Keywords:
signal processing ;  deep learning ;  deep belief network ;  sequence to sequence algorithm ;  combined model
分类号:
TM615
文献标志码:
A
摘要:
为进一步提高光伏发电超短期预测的精度,以数据分解重构和深度学习技术为依托,提出一种基于 CEEMDAN-DBN-Seq2Seq 的光伏发电功率超短期预测方法。首先利用具有自适应噪声的完整经验模态分解算法( CEEMDAN )将原始发电数据分解成在时域内特征更加明显的模态函数序列,以提取发电序列在时间尺度上的特征;随后引入影响光伏出力的主要气象因素,利用深度信念网络( DBN )对重构后的高、低频分量和序列对序列( Seq2Seq )方法对残差分量进行预测。实验表明,所提模型在光伏发电预测研究中精确度更高。
Abstract:
In order to further improve the accuracy of ultra-short-term photovoltaic power generation prediction , based on data decomposition and reconstruction and deep learning technology , an ultra short-term photovoltaic power prediction method based on CEEMDAN-DBN-Seq2Seq was proposed. Firstly , Complete Ensemble Empirical Mode Decomposition with Adaptive Noise( CEEMDAN ) is use to decomposes the original generation data into a series of modal functions with more obvious characteristics in the time domain to extract the characteristics of the generation sequence on the time scale.Then , Deep Belief Network( DBN ) and Sequence to Sequence( Seq2Seq ) methods were used to predict the reconstructed modal components respectively.The experiments show that the proposed model has higher accuracy in the prediction research of photovoltaic power generation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2021-11-23
基金项目:长安大学中央高校基本科研业务费专项资金资助( 300102321504 ,300102321501 ,300102321503 );西安市智慧高速公路信息融合与控制重点实验室( ZD13CG46 );陕西省重点研发计划( 2021GY-098 )
作者简介:徐先峰 ( 1982- ),男,山东泰安人,副教授,研究方向为深度学习理论及应用、交通能源融合理论、智能电网;郑少杰 ( 1997- ),男,福建莆田人,硕士研究生,研究方向为光伏故障诊断;赵 依 ( 1997- ),女,浙江杭州人,硕士研究生,研究方向为电力负荷预测;王世鑫 ( 1996- ),女,辽宁朝阳人,硕士研究生,研究方向为电力负荷预测;蔡路路 ( 1996- ),女,河南商丘人,硕士研究生,研究方向为新能源发电技术。
更新日期/Last Update: 2022-04-28